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This work analyzes the original same-step coordinate-wise AdaGrad under generalized smoothness and heavy-tailed noise with bounded variance and shows that clipping is a structural stabilizer of the adaptive geometry rather than merely a robustness heuristic for AdaGrad under heavy-tailed noise.
A simple and efficient self-supervised pre-training framework for 3D medical images based on a two-fold patch-wise perturbation strategy, requiring substantially less memory, computation, and training time than the state-of-the-art pre-training pipelines.
Results show that NVE is sensitive to redundant and poorly separated biclusters, while NVE changes solution rankings when low-error biclusters cover only a negligible part of the matrix, and suggest that NVE-based measures are useful complementary criteria for internal co-clustering validation, especially when coherenc...
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